"""simple docstring"""


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   , lowerCamelCase__   =image.size
						lowerCamelCase__   , lowerCamelCase__   =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						lowerCamelCase__   =image.resize((w, h)  ,       resample=PIL_INTERPOLATION["lanczos"]							)
						lowerCamelCase__   =np.array(__lowerCAmelCase							).astype(np.floataa							) / 255.0
						lowerCamelCase__   =image[None].transpose(0  ,       3  ,       1  ,       2							)
						lowerCamelCase__   =torch.from_numpy(__lowerCAmelCase							)
						return 2.0 * image - 1.0






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					):
										super().__init__()
										self.register_modules(vqvae=_lowerCamelCase      ,					unet=_lowerCamelCase      ,					scheduler=_lowerCamelCase )






				@torch.no_grad()
				def __call__( self      ,					_lowerCamelCase = None      ,					_lowerCamelCase = 1      ,					_lowerCamelCase = 100      ,					_lowerCamelCase = 0.0      ,					_lowerCamelCase = None      ,					_lowerCamelCase = "pil"      ,					_lowerCamelCase = True      ,					):
										if isinstance(_lowerCamelCase      ,					PIL.Image.Image ):
																lowerCamelCase__   =1
										elif isinstance(_lowerCamelCase      ,					torch.Tensor ):
																lowerCamelCase__   =image.shape[0]
										else:
																raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCamelCase )}''' )

										if isinstance(_lowerCamelCase      ,					PIL.Image.Image ):
																lowerCamelCase__   =preprocess(_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =image.shape[-2:]

										# in_channels should be 6: 3 for latents, 3 for low resolution image
										lowerCamelCase__   =(batch_size, self.unet.config.in_channels // 2, height, width)
										lowerCamelCase__   =next(self.unet.parameters() ).dtype

										lowerCamelCase__   =randn_tensor(_lowerCamelCase      ,					generator=_lowerCamelCase      ,					device=self.device      ,					dtype=_lowerCamelCase )

										lowerCamelCase__   =image.to(device=self.device      ,					dtype=_lowerCamelCase )

										# set timesteps and move to the correct device
										self.scheduler.set_timesteps(_lowerCamelCase      ,					device=self.device )
										lowerCamelCase__   =self.scheduler.timesteps

										# scale the initial noise by the standard deviation required by the scheduler
										lowerCamelCase__   =latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										lowerCamelCase__   ="eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
										lowerCamelCase__   ={}
										if accepts_eta:
																lowerCamelCase__   =eta

										for t in self.progress_bar(_lowerCamelCase ):
																# concat latents and low resolution image in the channel dimension.
																lowerCamelCase__   =torch.cat([latents, image]      ,					dim=1 )
																lowerCamelCase__   =self.scheduler.scale_model_input(_lowerCamelCase      ,					_lowerCamelCase )
																# predict the noise residual
																lowerCamelCase__   =self.unet(_lowerCamelCase      ,					_lowerCamelCase ).sample
																# compute the previous noisy sample x_t -> x_t-1
																lowerCamelCase__   =self.scheduler.step(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					**_lowerCamelCase ).prev_sample

										# decode the image latents with the VQVAE
										lowerCamelCase__   =self.vqvae.decode(_lowerCamelCase ).sample
										lowerCamelCase__   =torch.clamp(_lowerCamelCase      ,					-1.0      ,					1.0 )
										lowerCamelCase__   =image / 2 + 0.5
										lowerCamelCase__   =image.cpu().permute(0      ,					2      ,					3      ,					1 ).numpy()

										if output_type == "pil":
																lowerCamelCase__   =self.numpy_to_pil(_lowerCamelCase )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase )



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase  ,       __lowerCAmelCase							):
												raise TypeError("only integers accepted as input"							)
						else:
												lowerCamelCase__   =str(abs(__lowerCAmelCase							)							)
												lowerCamelCase__   =[list(__lowerCAmelCase							) for char in range(len(__lowerCAmelCase							)							)]
												for index in range(len(__lowerCAmelCase							)							):
																		num_transpositions[index].pop(__lowerCAmelCase							)
												return max(
												    int("".join(list(__lowerCAmelCase							)							)							) for transposition in num_transpositions							)


if __name__ == "__main__":
	__import__('doctest').testmod()


